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Record W2474393539 · doi:10.1017/s0008423916000366

Legislating Multiculturalism and Nationhood: The 1988<i>Canadian Multiculturalism Act</i>

2016· article· en· W2474393539 on OpenAlexaboutno aff
Varun Uberoi

Bibliographic record

VenueCanadian Journal of Political Science · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsnot available
FundersBritish Academy
KeywordsMulticulturalismLegislationPolitical sciencePoliticsEliteLawPublic administrationPolitical economySociologyLaw and economics

Abstract

fetched live from OpenAlex

Abstract In this article I use new archival and elite interview data to improve our knowledge of how the Canadian Multiculturalism Act came into existence. I show why some Canadians began to seek such an act, why political parties promised an act and how this act was created. The evidence in this article will also correct claims that scholars often make about this act and the policy of multiculturalism that it contains. This evidence also improves our knowledge of why the policy of multiculturalism in this legislation does what few scholars would expect. This is because scholars often claim that policies of multiculturalism are used to “repudiate” and remove understandings of a country. But my evidence helps to show why the policy of multiculturalism in this act promotes understandings of a country. Scholars also claim that policies of multiculturalism can be divisive if they are unaccompanied by nation-building policies. But my evidence helps to show why the policy of multiculturalism in this legislation was designed to be a nation-building policy.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.146
Threshold uncertainty score0.990

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0200.012
Scholarly communication0.0050.001
Open science0.0010.003
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.016
GPT teacher head0.257
Teacher spread0.241 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations17
Published2016
Admission routes1
Has abstractyes

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